If the reward function of an environment depends on some initial conditions, should I create a separate environment for each condition?
In this scenario, is it preferable to create two separate environments based on the priority (input parameter) of the alert?

It is difficult to make a hard rule here.
If the resulting environments can be cleanly sorted into a few different categories, and the ideal behaviour and/or the states visited are radically different in each scenario, then maybe a few different agents optimised for each scenario could work well.
A more general approach however, is to include the episode start data as part of the state that the agent observes on each time step. A single agent can then in theory learn the different behaviours required depending on the initial values, plus still generalise from anything shared between the multiple scenarios.

The alert has two priorities &quot;HIGH&quot; and &quot;LOW&quot;, when the priority is &quot;HIGH&quot; the stopping condition is a reward of &quot;100&quot; and when the priority is &quot;LOW&quot;, the stopping condition is a reward of &quot;1000&quot;.

This may work against you. RL agents do not respond to the absolute values of rewards, other than how they compare to other rewards also available within the same episode (or continuing environment).
If there is only ever one issue to solve at a time, and no conflict between solving either of the &quot;HIGH&quot; or &quot;LOW&quot; priority problems (such as splittig resources or effort between them), the different reward system seems redundant. Solved is solved. You might rate the usefulness of an agent that solves the &quot;LOW&quot; priority issue well higher, but it seems to me that this describes what you should work on first, not the goals of the agent. To influence the goals of the agent, both rewards would need to be available within the same episode or continuing environment, requiring the agent to make a choice between them.